---
title: "Discussion on \"DICOM Segmentation Quality: What “Good Enough” Labels Look Like for Radiology AI\" | Hashnode"
description: "Discussion on \"DICOM Segmentation Quality: What “Good Enough” Labels Look Like for Radiology AI\". Radiology AI projects often treat segmentation as a pixel problem. Draw the organ, draw the lesion, export the mask, train the model. In practice, most failures that show up late in validation started"
url: "https://hashnode.com/posts/dicom-segmentation-quality-radiology-ai/6aeeb2c24da4027f28467fea"
---

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[MedAnnotate](https://hashnode.com/@medannotate)

Medical data annotation services

2d ago

# [DICOM Segmentation Quality: What “Good Enough” Labels Look Like for Radiology AI](https://medannotate.hashnode.dev/dicom-segmentation-quality-radiology-ai?utm_source=hashnode&utm_medium=feed)

Radiology AI projects often treat segmentation as a pixel problem. Draw the organ, draw the lesion, export the mask, train the model. In practice, most failures that show up late in validation started

medannotate.hashnode.dev 4 min read

[#healthcare](https://hashnode.com/tag/healthcare) [#medical-ai](https://hashnode.com/tag/medical-ai) [#radiology](https://hashnode.com/tag/radiology) [#dicom](https://hashnode.com/tag/dicom) [#data-annotation](https://hashnode.com/tag/data-annotation)

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